Predictive Methods in Next-Generation Computing

Name: Predictive Methods in Next-Generation Computing | An Approach Toward Sustainability
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An Approach Toward Sustainability

R. Sathiyaraj Rajesh Kumar Dhanaraj K. Arun Kumar Rutvij H. Jhaveri A. Mohamed Abbas(Editor)

Wiley (Publisher)

1st Edition

Published on 26. February 2026

341 pages

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978-1-394-24881-0 (ISBN)

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Content

Cover
Series Page
Title Page
Copyright Page
Contents
Preface
Chapter 1 Introduction to Intelligent Computational Technologies
1.1 Introduction
1.2 Literature Survey
1.3 Methodology
1.4 Simulation Metrics
1.4.1 Identification of E-Governance Adoption and Assessment Factors
1.4.2 Sample Data Collection Using Questionnaire
1.4.3 Respondent Details
1.4.4 Fuzzy Conjoint Model
1.4.4.1 Calculation of Weight for Each Respondent
1.4.4.2 Calculating the Similarity Degree
1.5 Computation
1.5.1 Computation of Fuzzy Vector of Responses
1.5.2 Computation of Similarity Degree
1.5.3 Result Analysis
1.5.4 Validation
1.5.5 Limitations and Future Study
1.6 Summary
Bibliography
Chapter 2 Design of Smart and Sustainable Applications Using Intelligent Computational Techniques
2.1 Introduction
2.2 Background
2.2.1 Adoption Models and Theories
2.3 Methodology
2.3.1 Simulation Metrics
2.3.2 Identification of E-Governance Adoption and Assessment Factors
2.3.3 Sample Data Collection Using Questionnaire
2.3.4 Respondent Details
2.3.5 Fuzzy Conjoint Model
2.3.5.1 Calculating the Similarity Degree
2.4 Result Analysis
2.5 Conclusion
References
Chapter 3 Intelligent Predictive Analysis for Sustainable Global Development
3.1 Introduction
3.2 Literature Survey
3.2.1 Concepts of ANN
3.3 Proposed Work
3.3.1 Layout of a Neural Network
3.3.2 Neural Network Structure
3.3.3 Back-Propagation Neural Network
3.3.4 SVM
3.3.5 Learning Sequence of NN
3.3.6 Forage Progressive Network
3.3.7 Neural Network Learning
3.3.8 Model Somatic Cell
3.3.9 Fabricated Visual Structure
3.3.10 Neuron Weight Adjustment
3.4 Results and Discussion
3.4.1 Normalization of Knowledge
3.4.2 Testing and Validation
3.4.3 Error Measures
3.4.4 Prediction Analysis
3.4.5 Overall Prediction Analysis
3.4.6 Actual and Predicted Value Analysis
3.4.7 Overall Actual and Predicted Value Analysis
3.5 Summary
References
Chapter 4 Intelligent Transport System and Traffic Management Frameworks
4.1 Introduction
4.2 Background Study
4.3 Methodology
4.3.1 Segmentation-Fuzzy Clustering
4.3.2 Fuzzy Clustering
4.4 Artificial Neural Network (ANN)
4.5 Proposed Methodology
4.5.1 Detection and Extraction of Traffic Sign
4.5.1.1 Image Extraction and Pre-Processing Using YCbCr
4.5.1.2 Active Appearance Model (AAM)
4.5.1.3 Extracting the Region of Interest
4.5.1.4 Edge Detection Using Sobel Operator
4.5.1.5 Segmentation Using Adaptive Fuzzy Clustering
4.5.1.6 Tracking the Detected Sign
4.5.2 Recognition of Traffic Sign
4.5.2.1 MTANN Training Model for Classification
4.5.2.2 Multiple MTANN Training Models
4.5.2.3 MTANN Classification
4.6 Results and Discussion
4.6.1 LiU Traffic Sign Database
4.6.2 Investigations of Various Classification Techniques
4.7 Summary
References
Chapter 5 Internet of Things in Smart and Secure Applications Development-Based Sustainability
5.1 Introduction
5.2 Literature Survey
5.3 Methodology
5.3.1 Module for the Database
5.3.2 Information Preparation Section
5.3.3 Database Module
5.3.4 Data Preprocessing Module
5.3.5 Optimal Feature Selection Module
5.3.6 Classification Module
5.4 Generative Adversarial Network
5.5 Datasets Used in This Work
5.5.1 NSL-KDD Dataset
5.5.2 CIC-DDoS Dataset
5.6 Performance Measures Used for Evaluation
5.7 Conclusion
References
Chapter 6 Modern Application for Smart Applications in Traffic Management
6.1 Introduction
6.2 Related Work
6.3 Delimited Spaces: Proposed Method
6.3.1 Bag of Features (BoF)
6.4 Implementation Details
6.4.1 Datasets
6.4.2 Experiment 2: Original and Split Dictionaries
6.4.3 Experiment 3: Feature Fusion
6.4.4 Results: Delimited Spaces
6.5 Non-Delimited Spaces: Proposed Method
6.5.1 Background Subtraction for Hypothesis Generation
6.5.2 Results: Non-Delimited Spaces
6.6 Conclusion
References
Chapter 7 Artificial Intelligence and Deep Learning in Healthcare: Evaluation, Opportunities, Challenges and Future Prospects Technologies in Healthcare Systems
7.1 Introduction
7.1.1 Artificial Intelligence
7.1.2 Deep Intelligence
7.2 Applications of AI and Deep Learning in Healthcare
7.2.1 Medical Imaging
7.2.2 Personalized Medicine
7.2.3 Precision Diagnostics
7.2.4 Predictive Analytics
7.2.5 Drug Discovery
7.2.6 Drug Development
7.2.7 Remote Patient Monitoring
7.2.8 Electronic Health Records
7.3 Development of Deep Learning and Artificial Intelligence in Healthcare Sector
7.4 Analytics of Healthcare Data Through AI And DL
7.4.1 Machine Learning Models
7.4.1.1 Supervised Learning
7.4.1.2 Unsupervised Learning
7.4.2 Reinforcement Learning
7.4.3 Natural Language Processing (NLP)
7.4.4 Machine Vision
7.4.5 Data Mining
7.4.6 Artificial Neural Networks
7.4.7 Fuzzy Logic
7.4.8 Expert System
7.5 Deep Learning Models
7.5.1 Convolutional Neural Networks (CNNs)
7.5.2 Network Based on Long Short-Term Memory (LSTM)
7.5.3 Recurrent Neural Networks (RNNs)
7.5.4 Generative Adversarial Network (GANs)
7.5.5 Radial Basis Function Networks (RBFNs)
7.5.6 Multilayer Perceptrons (MLPs)
7.5.7 Self-Organizing Maps (SOMs)
7.6 Potential of AI and Deep Learning Models in Healthcare
7.7 The Rise of AI and DL in Drug Discovery
7.8 Application of AI in Drug Discovery
7.9 Challenges of AI And DL Models
7.10 Future Vision in Developing Rural Health
7.10.1 Telemedicine
7.10.2 Disease Prediction and Prevention
7.10.3 Resource Allocation
7.10.4 Personalized Medicine
7.11 Conclusion
References
Chapter 8 Heart CAP: Heart Disease Classification: Autoencoders and Principal Components
8.1 Introduction
8.2 Related Study
8.3 Model Architecture
8.3.1 Cleveland Heart Disease Dataset
8.3.2 Data Preparation
8.3.3 Feature Scaling
8.3.4 Dimensionality Reduction Techniques
8.3.4.1 Principal Component Analysis (PCA)
8.3.4.2 Autoencoder Architecture
8.4 Results
8.4.1 Classification Performance
8.4.2 Analysis of Principal Component and Feature Coefficients
8.4.3 Analysis of Autoencoder Features
8.4.4 Analysis of Receiver Operating Characteristics
8.5 Conclusion
References
Chapter 9 Application of Intelligent Computational Techniques in the Development of Smart Cities
9.1 Introduction
9.1.1 Organization of the Chapter
9.2 Motivation and Justification
9.3 Iris Recognition System
9.4 Algorithm for Iris Recognition System
9.5 Block Diagram of Iris Recognition System
9.5.1 Edge Detection
9.5.2 Variance
9.5.3 Support Vector Machine
9.6 Performance Analysis of Various Biometric Method
9.6.1 Performance Evaluation
9.6.1.1 False Acceptance Ratio (FAR)
9.6.1.2 False Rejection Ratio (FRR)
9.6.2 Result and Discussion
9.7 Summary
Bibliography
Chapter 10 Security and Privacy Issues in Data Processing with Predictive Models
10.1 Introduction
10.2 Literature Survey
10.3 System Model
10.3.1 Cryptic Framework
10.3.2 Prediction Framework
10.4 System Algorithm
10.5 Results and Discussion
10.5.1 Examining the Security Model's Performance
10.5.2 Duration of Key Generation
10.5.3 Performance Analysis of Prediction Model
10.5.3.1 Description of the Dataset
10.5.4 Performance Metrics
10.5.4.1 Sensitivity
10.5.4.2 Specificity
10.5.4.3 F-Measure
10.5.4.4 Diabetes Prediction
10.6 Summary of Contributions
References
Chapter 11 SmartMed: A Blockchain-Based Intelligent System for Managing Patient Data
11.1 Introduction
11.1.1 The Need for the Digitization of Medical Records
11.1.2 What is Blockchain?
11.1.3 How to Use Blockchain to Digitize Medical Records
11.2 Literature Survey
11.2.1 State-of-the-Art
11.2.2 Research Gap
11.3 SmartMed: Proposed System
11.4 SmartMed: Model Implementation
11.4.1 Software Requirements
11.4.2 User Registration
11.4.3 Login
11.4.4 Upload Records
11.4.5 View Records
11.4.6 Grant/Revoke Permissions
11.4.7 Encryption of Medical Records
11.4.8 Need for Encryption
11.4.9 Symmetric vs. Asymmetric Encryption
11.5 Result Analysis Using Hybrid Model
11.5.1 Scenario 1: Patient Uploads a Record
11.5.2 Scenario 2: Patient Views the Uploaded Record
11.5.3 Scenario 3: Patient Grants Access to His/Her Records to a Doctor
11.5.4 Scenario 4: Patient Revokes Access to His/Her Records from a Doctor
11.5.5 Scenario 5: Doctor Views the Patient Record
11.5.6 Scenario 6: Doctor Uploads a Record for the Patient
11.6 SmartMed: Performance Evaluation
11.6.1 Latency/Delay
11.6.2 Resource Utilization
11.7 Conclusion and Perspective
References
Chapter 12 Trinity: A Blockchain-Based Stablecoin Lending Protocol Using Decentralized Credit Default Swaps
12.1 Introduction
12.1.1 Background and Related Work
12.2 Motivation and Problem Statement
12.3 Methodology
12.3.1 Option Fees Exchanged Among the Participants
12.4 Discussions
12.4.1 Stakeholders in Protocol
12.4.2 Tokens in Protocol
12.4.3 Components of the Protocol
12.4.4 Option Price Calculation
12.4.5 Risk Management and Protocol Tokens/Reserve Currency
12.4.5.1 Use of Protocol Tokens/Reserve Currency in Risk Management
12.4.5.2 Managing Liquidation in Protocol
12.4.5.3 Liquidation Management
12.4.5.4 Calculation of dCDS Holder Accrued Amount During Withdrawal/Close
12.4.6 Utilization of Protocol Tokens as Reserve Currency
12.5 Conclusion
Appendix
References
Chapter 13 Renewable Energy Integration in Data Centers: Strategies and Challenges
13.1 Introduction
13.2 Renewable Generation Technologies
13.2.1 Photovoltaic Solar
13.2.2 Wind Power
13.2.3 Geothermal Heat
13.3 Impacts of Supply Variability
13.3.1 Power Distribution
13.3.2 Cooling Systems
13.3.3 IT Equipment
13.4 Network Architecture
13.4.1 Renewable Energy Integration Methods
13.4.2 Task Time Management
13.4.3 Integrated Architectures
13.5 Proposed Smart and Sustainable Framework
13.5.1 Geo-Distributed Load Balancing
13.6 Conclusions
References
Chapter 14 Optimizing Energy Efficiency in Computing Systems: An In-Depth Exploration: Analytics for Sustainable Global Resources
14.1 Introduction
14.2 Energy-Efficient Hardware and Software in Computing Systems
14.3 Smart Solutions to Optimize Energy Efficiency in Computing Systems
14.3.1 Low-Power Processors
14.3.2 Low-Power Memory
14.3.3 On-Chip Interconnects
14.3.4 Hardware Accelerators
14.3.5 Operating Systems
14.3.6 Compilers and Runtimes
14.3.7 Algorithms and Applications
14.3.8 Energy-Efficient Data Centers
14.4 Augmentations of Artificial Intelligence (AI)/ Machine Learning (ML)/IoT in Optimizing Energy Efficiency in Computing Systems
14.5 Sustainable Computing
14.6 Conclusions
Acknowledgment
References
About the Editors
Index
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Predictive Methods in Next-Generation Computing

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